β Scribed by Ethem Alpaydin
No coin nor oath required. For personal study only.
I've read several parts of chapters which concerned my work and skimmed other chapters faster. This book should serve as a starting point and mostly as a quick introduction in a subject. However, i've found this book to be useful in other way - it is compact and I found several basic reasonements and assumptions quickly to base my conclusions in work i was doing. Also i like the style where key-words appear outside the text where they can be easily spotted and also the references at the end of each chapter.
Contents......Page 7
Series Foreword......Page 17
Figures......Page 19
Tables......Page 29
Preface......Page 31
Acknowledgments......Page 33
Notes for the Second Edition......Page 35
Notations......Page 39
1 Introduction......Page 41
2 Supervised Learning......Page 61
3 Bayesian Decision Theory......Page 87
4 Parametric Methods......Page 101
5 Multivariate Methods......Page 127
6 Dimensionality Reduction......Page 149
7 Clustering......Page 183
8 Nonparametric Methods......Page 203
9 Decision Trees......Page 225
10 Linear Discrimination......Page 249
11 Multilayer Perceptrons......Page 273
12 Local Models......Page 319
13 Kernel Machines......Page 349
14 Bayesian Estimation......Page 381
15 Hidden Markov Models......Page 403
16 Graphical Models......Page 427
17 Combining Multiple Learners
......Page 459
18 Reinforcement Learning......Page 487
19 Design and Analysis of Machine Learning Experiments......Page 515
A Probability......Page 557
Index......Page 569
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